Simulating quantum many-body systems represents one of the central challenges in theoretical physics due to the exponential growth of computational complexity in high-dimensional Hilbert spaces. While significant progress has been made using specialized frameworks for specific regimes, we still lack a comprehensive approach that effectively connects these various methods and synergizes experimental data with computational results. Recent breakthroughs in artificial intelligence, particularly in machine learning and large language models, offer promising new pathways for representing quantum states, analyzing complex data, and automating aspects of both experimental and theoretical research.

This program aims to explore the intersection of AI and quantum matter across three key areas: machine learning simulations of quantum many-body ground states and dynamics; AI-assisted quantum control and automation; and machine learning methods for analyzing quantum data. By bringing together quantum physicists, computer scientists, and experimentalists, we intend to advance neural network-based wavefunction representations, develop frameworks for sharing pre-trained quantum automation models, establish cross-platform experimental databases, create AI research assistants for specialized tasks, and design quantum learning algorithms with theoretical guarantees. Through these collaborative efforts, we seek to forge new connections between AI and quantum physics that can drive transformative progress in understanding quantum matter.